Single vs triple injections of platelet-rich plasma in patients with knee osteoarthritis: a retrospective cohort study of 80 patients
Bibliographic record
Abstract
Introduction Osteoarthritis (OA) is a prevalent degenerative joint disorder that significantly impairs quality of life. Platelet-rich plasma (PRP) has emerged as a promising treatment option for managing symptoms of knee osteoarthritis. Objectives The aim of this study is to compare the effects of triple versus single PRP injections on pain and quality of life in individuals with knee osteoarthritis. Methods The files of 40 patients who received a single dose of PRP and 40 patients who received three doses of PRP for the treatment of knee osteoarthritis were retrospectively examined. Patients had been evaluated before the treatment and at their 6 month follow up with a Visual Analog Scale (VAS) and The Western Ontario and McMaster Universities (WOMAC) Osteoarthritis Index. Results At the 6-month examinations, there was a statistically significant decrease in the WOMAC total (single, from 64.7 ± 13.2 to 54.4 ± 17.2, P = .001; triple, from 67.0 ± 14.7 to 45.4 ± 19.2, P = .001), and VAS scores (single, from 7.1 ± 1.0 to 5.10 ± 2.0, P = .002; and triple, from 7.3 ± 1.0 to 3.7 ± 2.2, P = .001) of both the single and triple injection groups compared to the baseline. WOMAC total ( P = .03) and VAS scores ( P = .006) of the triple injection group were significantly better but there was no significant difference in WOMAC pain ( P = .06), WOMAC physical ( P = .06) and WOMAC stiffness (0.08) subscales. Conclusions This research revealed that triple PRP injections are more effective than single injections at improving functional outcomes. Furthermore, the potential to alleviate pain may be greater with multiple injections; however, this effect was not consistently observed across all pain evaluation metrics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".